  Semi-supervised medical image segmentation has gained growing interest due to
its ability to utilize unannotated data. The current state-of-the-art methods
mostly rely on pseudo-labeling within a co-training framework. These methods
depend on a single pseudo-label for training, but these labels are not as
accurate as the ground truth of labeled data. Relying solely on one
pseudo-label often results in suboptimal results. To this end, we propose a
novel approach where multiple pseudo-labels for the same unannotated image are
used to learn from the unlabeled data: the conventional fixed pseudo-label and
the newly introduced dynamic pseudo-label. By incorporating multiple
pseudo-labels for the same unannotated image into the co-training framework,
our approach provides a more robust training approach that improves model
performance and generalization capabilities. We validate our novel approach on
three semi-supervised medical benchmark segmentation datasets, the Left Atrium
dataset, the Pancreas-CT dataset, and the Brats-2019 dataset. Our approach
significantly outperforms state-of-the-art methods over multiple medical
benchmark segmentation datasets with different labeled data ratios. We also
present several ablation experiments to demonstrate the effectiveness of
various components used in our approach.
