This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions on MNIST and Fashion-MNIST. In these settings, the proposed pooling consistently improves accuracy and weighted F1 over conventional baselines, with particularly strong gains when training data are scarce. Even with less than 1% of the training set, our method maintains reliable performance, indicating improved sample efficiency and robustness to noisy or ambiguous local patterns. Overall, integrating soft memberships into the pooling operator provides a practical and generalizable inductive bias that enhances robustness and generalization in modern CNN pipelines.