Researchers propose EviDep, a multimodal framework for estimating depression severity from audio-visual recordings, using disentangled evidential learning and uncertainty-aware regression. The framework integrates multi-scale temporal modeling and shared-private representation learning. Experiments show competitive prediction accuracy and the utility of estimated uncertainty in identifying higher-error predictions.