EnTrust introduces a framework that treats inter-modal conflict as the primary source of predictive uncertainty in medical image analysis. It decomposes multimodal features into shared consensus, modality-specific cues, and conflict signals, enabling calibrated, pixel-wise uncertainty estimation through a diffusion-based model and trust mapping. EnTrust achieves state-of-the-art segmentation accuracy, reduces calibration error by 40%, and outperforms 5x deep ensembles with half the memory footprint.