Precise retina Optical Coherence Tomography (OCT) image classification and segmentation are important for di-agnosing various retinal diseases and identifying specific regions. Alongside comprehensive lesion identification, re-ducing the predictive uncertainty of models is crucial for improving reliability in clinical retinal practice. However, existing methods have primarily focused on a limited set of regions identified in OCT images and have often faced challenges due to aleatoric and epistemic uncertainty. To address these issues, we propose CAMEL (Confidence-Aware Multi-task Ensemble Learning), a novel frame-work designed to reduce task-specific uncertainty in multi-task learning. CAMEL achieves this by estimating model confidence at both pixel and image levels and leveraging confidence-aware ensemble learning to minimize the un-certainty inherent in single-model predictions. CAMEL demonstrates state-of-the-art performance on a compre-hensive retinal OCT image dataset containing annotations for nine distinct retinal regions and nine retinal diseases. Furthermore, extensive experiments highlight the clini-cal utility of CAMEL, especially in scenarios with mini-mal regions, significant class imbalances, and diverse re-gions and diseases. Our code is publicly available at: https://github.com/DSAIL-SKKU/CAMEL.