  A versatile medical image segmentation model applicable to images acquired
with diverse equipment and protocols can facilitate model deployment and
maintenance. However, building such a model typically demands a large, diverse,
and fully annotated dataset, which is challenging to obtain due to the
labor-intensive nature of data curation. To address this challenge, we propose
a cost-effective alternative that harnesses multi-source data with only partial
or sparse segmentation labels for training, substantially reducing the cost of
developing a versatile model. We devise strategies for model
self-disambiguation, prior knowledge incorporation, and imbalance mitigation to
tackle challenges associated with inconsistently labeled multi-source data,
including label ambiguity and modality, dataset, and class imbalances.
Experimental results on a multi-modal dataset compiled from eight different
sources for abdominal structure segmentation have demonstrated the
effectiveness and superior performance of our method compared to
state-of-the-art alternative approaches. We anticipate that its cost-saving
features, which optimize the utilization of existing annotated data and reduce
annotation efforts for new data, will have a significant impact in the field.
