The accurate classification of cervical cytology in Pap smear images remains a critical challenge in computer-aided diagnosis, largely due to the inherent uncertainty and subtle morphological variations among different pathological categories. To address this, we propose a novel uncertainty-aware ensemble framework that integrates statistical quantile analysis with deep learning for robust and interpretable classification. Our framework first leverages three deep convolutional neural networks DenseNet121, MobileNetV2, and ResNet-50 as base feature extractors. Instead of employing naive ensemble strategies, we introduce a quantile deviation based weighting mechanism to dynamically assess and... More >