diff --git a/doc/source/whatsnew/v2.0.1.rst b/doc/source/whatsnew/v2.0.1.rst
index e9e1881e879da..2e8e2345d4c0a 100644
--- a/doc/source/whatsnew/v2.0.1.rst
+++ b/doc/source/whatsnew/v2.0.1.rst
@@ -27,6 +27,7 @@ Bug fixes
 - Bug in :attr:`Series.dt.days` that would overflow ``int32`` number of days (:issue:`52391`)
 - Bug in :class:`arrays.DatetimeArray` constructor returning an incorrect unit when passed a non-nanosecond numpy datetime array (:issue:`52555`)
 - Bug in :func:`Series.median` with :class:`ArrowDtype` returning an approximate median (:issue:`52679`)
+- Bug in :func:`api.interchange.from_dataframe` was unnecessarily raising on-categorical dtypes (:issue:`49889`)
 - Bug in :func:`pandas.testing.assert_series_equal` where ``check_dtype=False`` would still raise for datetime or timedelta types with different resolutions (:issue:`52449`)
 - Bug in :func:`read_csv` casting PyArrow datetimes to NumPy when ``dtype_backend="pyarrow"`` and ``parse_dates`` is set causing a performance bottleneck in the process (:issue:`52546`)
 - Bug in :func:`to_datetime` and :func:`to_timedelta` when trying to convert numeric data with a :class:`ArrowDtype` (:issue:`52425`)
diff --git a/pandas/core/interchange/from_dataframe.py b/pandas/core/interchange/from_dataframe.py
index 065387e1acefd..2bbb678516968 100644
--- a/pandas/core/interchange/from_dataframe.py
+++ b/pandas/core/interchange/from_dataframe.py
@@ -7,7 +7,6 @@
 import numpy as np
 
 import pandas as pd
-from pandas.core.interchange.column import PandasColumn
 from pandas.core.interchange.dataframe_protocol import (
     Buffer,
     Column,
@@ -181,9 +180,15 @@ def categorical_column_to_series(col: Column) -> tuple[pd.Series, Any]:
         raise NotImplementedError("Non-dictionary categoricals not supported yet")
 
     cat_column = categorical["categories"]
-    # for mypy/pyright
-    assert isinstance(cat_column, PandasColumn), "categories must be a PandasColumn"
-    categories = np.array(cat_column._col)
+    if hasattr(cat_column, "_col"):
+        # Item "Column" of "Optional[Column]" has no attribute "_col"
+        # Item "None" of "Optional[Column]" has no attribute "_col"
+        categories = np.array(cat_column._col)  # type: ignore[union-attr]
+    else:
+        raise NotImplementedError(
+            "Interchanging categorical columns isn't supported yet, and our "
+            "fallback of using the `col._col` attribute (a ndarray) failed."
+        )
     buffers = col.get_buffers()
 
     codes_buff, codes_dtype = buffers["data"]
diff --git a/pandas/tests/interchange/test_impl.py b/pandas/tests/interchange/test_impl.py
index 078a17510d502..abdfb4e79cb20 100644
--- a/pandas/tests/interchange/test_impl.py
+++ b/pandas/tests/interchange/test_impl.py
@@ -74,6 +74,21 @@ def test_categorical_dtype(data):
     tm.assert_frame_equal(df, from_dataframe(df.__dataframe__()))
 
 
+def test_categorical_pyarrow():
+    # GH 49889
+    pa = pytest.importorskip("pyarrow", "11.0.0")
+
+    arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun"]
+    table = pa.table({"weekday": pa.array(arr).dictionary_encode()})
+    exchange_df = table.__dataframe__()
+    result = from_dataframe(exchange_df)
+    weekday = pd.Categorical(
+        arr, categories=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+    )
+    expected = pd.DataFrame({"weekday": weekday})
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "data", [int_data, uint_data, float_data, bool_data, datetime_data]
 )