diff --git a/pandas/core/indexes/category.py b/pandas/core/indexes/category.py
index f06d118538c1a..287f94fb6b723 100644
--- a/pandas/core/indexes/category.py
+++ b/pandas/core/indexes/category.py
@@ -365,7 +365,6 @@ def __contains__(self, key: Any) -> bool:
 
         return contains(self, key, container=self._engine)
 
-    # TODO(2.0): remove reindex once non-unique deprecation is enforced
     def reindex(
         self, target, method=None, level=None, limit=None, tolerance=None
     ) -> tuple[Index, npt.NDArray[np.intp] | None]:
@@ -392,51 +391,7 @@ def reindex(
             raise NotImplementedError(
                 "argument limit is not implemented for CategoricalIndex.reindex"
             )
-
-        target = ibase.ensure_index(target)
-
-        if self.equals(target):
-            indexer = None
-            missing = np.array([], dtype=np.intp)
-        else:
-            indexer, missing = self.get_indexer_non_unique(target)
-            if not self.is_unique:
-                # GH#42568
-                raise ValueError("cannot reindex on an axis with duplicate labels")
-
-        new_target: Index
-        if len(self) and indexer is not None:
-            new_target = self.take(indexer)
-        else:
-            new_target = target
-
-        # filling in missing if needed
-        if len(missing):
-            cats = self.categories.get_indexer(target)
-
-            if not isinstance(target, CategoricalIndex) or (cats == -1).any():
-                new_target, indexer, _ = super()._reindex_non_unique(target)
-            else:
-                # error: "Index" has no attribute "codes"
-                codes = new_target.codes.copy()  # type: ignore[attr-defined]
-                codes[indexer == -1] = cats[missing]
-                cat = self._data._from_backing_data(codes)
-                new_target = type(self)._simple_new(cat, name=self.name)
-
-        # we always want to return an Index type here
-        # to be consistent with .reindex for other index types (e.g. they don't
-        # coerce based on the actual values, only on the dtype)
-        # unless we had an initial Categorical to begin with
-        # in which case we are going to conform to the passed Categorical
-        if is_categorical_dtype(target):
-            cat = Categorical(new_target, dtype=target.dtype)
-            new_target = type(self)._simple_new(cat, name=self.name)
-        else:
-            # e.g. test_reindex_with_categoricalindex, test_reindex_duplicate_target
-            new_target_array = np.asarray(new_target)
-            new_target = Index._with_infer(new_target_array, name=self.name)
-
-        return new_target, indexer
+        return super().reindex(target)
 
     # --------------------------------------------------------------------
     # Indexing Methods