Reliable biomedical image classification is essential for computer-aided diagnosis systems, where incorrect or overconfident predictions may lead to serious clinical consequences. Although deep convolutional neural networks (CNNs) have achieved impressive performance in medical image analysis, individual models often suffer from limited robustness under class imbalance, inter-class similarity, and uncertainty caused by imaging artifacts. Ensemble learning partially mitigates these issues; however, most existing ensemble strategies rely on static fusion rules that fail to account for sample-wise model reliability. In this paper, we present an UncertaintyAware Fuzzy Ensemble (UAFE) framework for biomedical image classification. The proposed approach dynamically assigns voting weights to heterogeneous CNN models using calibrated confidence and entropy- based uncertainty within a fuzzy inference system that improves decision stability and interpretability by emphasizing reliable predictions while suppressing uncertain ones. The framework is evaluated on two clinically significant and complementary datasets i.e. a Chest X-ray dataset for pulmonary disease classification and a Brain MRI dataset for neurological disorder classification. Experimental results demonstrate that the proposed UAFE framework consistently outperforms individual CNNs and conventional voting-based ensembles in terms of accuracy, F1score, and prediction confidence. These findings highlight the effectiveness of confidence-driven fuzzy ensemble learning for robust and trustworthy medical image analysis.