  The lack of sufficient annotated image data is a common issue in medical
image segmentation. For some organs and densities, the annotation may be
scarce, leading to poor model training convergence, while other organs have
plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based
meta-learning algorithm that redefines the meta-learning task for the
volumetric medical data with the goal to capture the variety between the
slices. We also explore different weighting schemes for gradients aggregation,
arguing that different tasks might have different complexity, and hence,
contribute differently to the initialization. We propose an importance-aware
weighting scheme to train our model. In the experiments, we present an
evaluation of the medical decathlon dataset by extracting 2D slices from CT and
MRI volumes of different organs and performing semantic segmentation. The
results show that our proposed volumetric task definition leads to up to 30%
improvement in terms of IoU compared to related baselines. The proposed update
rule is also shown to improve the performance for complex scenarios where the
data distribution of the target organ is very different from the source organs.
