Polyp segmentation in colonoscopy plays a crucial role in early colorectal cancer diagnosis, requiring efficient and accurate models to ensure clinical deployment. In this paper, we propose AURA-Net (Adaptive Uncertainty-weighted Ranking and Attention-driven Network), an innovative Mixture-of-Encoders framework for robust polyp segmentation. Our method integrates multiple encoder networks, including ResNetV2-50, EfficientNetV2-S, DenseNet-121, and MobileNetV2, with a pixel-wise spatial soft-gating mechanism that adaptively assigns weights to predictions based on local uncertainty. Additionally, we introduce the Spatial-Enhanced Edge Attention (SEEA) module, which refines boundary features without adding significant computational overhead, and the Adaptive Contrastive-Correlation Loss (AC \(^2\) L) to improve segmentation performance by balancing encoder diversity and expert-ensemble consistency. Extensive evaluations on the Kvasir-SEG dataset demonstrate the superiority of AURA-Net over state-of-the-art methods, achieving a mean IoU of 0.85, F2 score of 0.90, and a segmentation accuracy of 91.5%. We also analyze the model’s performance in an out-of-distribution (OOD) scenario, where AURA-Net exhibits a robust accuracy drop of only 3%, compared to a 15% degradation observed in traditional models. These results highlight the effectiveness of AURA-Net in not only achieving high accuracy but also maintaining reliability when faced with unseen data, making it a promising solution for real-time clinical applications.