  Despite the recent progress in medical image segmentation with scribble-based
annotations, the segmentation results of most models are still not ro-bust and
generalizable enough in open environments. Evidential deep learn-ing (EDL) has
recently been proposed as a promising solution to model predictive uncertainty
and improve the reliability of medical image segmen-tation. However directly
applying EDL to scribble-supervised medical im-age segmentation faces a
tradeoff between accuracy and reliability. To ad-dress the challenge, we
propose a novel framework called Dual-Branch Evi-dential Deep Learning (DuEDL).
Firstly, the decoder of the segmentation network is changed to two different
branches, and the evidence of the two branches is fused to generate
high-quality pseudo-labels. Then the frame-work applies partial evidence loss
and two-branch consistent loss for joint training of the model to adapt to the
scribble supervision learning. The pro-posed method was tested on two cardiac
datasets: ACDC and MSCMRseg. The results show that our method significantly
enhances the reliability and generalization ability of the model without
sacrificing accuracy, outper-forming state-of-the-art baselines. The code is
available at https://github.com/Gardnery/DuEDL.
