Modeling Uncertainty from Missing Modalities for Brain Tumor Segmentation

Brain tumor segmentation from multimodal MRIs is challenging when one or more modalities are missing. Many existing methods focus on multimodal representation learning or knowledge distillation but pay limited attention to how missing modalities affect predictions and the uncertainty associated with them. In this work, we study uncertainty from missing modalities under partial observation. From a probabilistic perspective, we characterize uncertainty from missing modalities by the total conditional variance of the posterior under full observation and show that this quantity equals the expected squared change between the class posteriors under full and partial observations. Building on this insight, we adopt Dirichlet modeling for predictions under missing modalities and learn the Dirichlet variance by matching it to the discrepancy between predictions under full and partial observations. This allows the estimated uncertainty to reflect the effect of missing modalities on the current prediction. Extensive experiments on BraTS2020 and BraTS2024 demonstrate that our method achieves strong performance across diverse missing-modality settings. Uncertainty analyses show closer agreement between the estimated uncertainty and the prediction change caused by missing modalities.