  3D medical image segmentation is a challenging task with crucial implications
for disease diagnosis and treatment planning. Recent advances in deep learning
have significantly enhanced fully supervised medical image segmentation.
However, this approach heavily relies on labor-intensive and time-consuming
fully annotated ground-truth labels, particularly for 3D volumes. To overcome
this limitation, we propose a novel probabilistic-aware weakly supervised
learning pipeline, specifically designed for 3D medical imaging. Our pipeline
integrates three innovative components: a probability-based pseudo-label
generation technique for synthesizing dense segmentation masks from sparse
annotations, a Probabilistic Multi-head Self-Attention network for robust
feature extraction within our Probabilistic Transformer Network, and a
Probability-informed Segmentation Loss Function to enhance training with
annotation confidence. Demonstrating significant advances, our approach not
only rivals the performance of fully supervised methods but also surpasses
existing weakly supervised methods in CT and MRI datasets, achieving up to
18.1% improvement in Dice scores for certain organs. The code is available at
https://github.com/runminjiang/PW4MedSeg.
