  Efficiently generating sufficient labeled data remains a major bottleneck in
deep learning, particularly for image segmentation tasks where labeling
requires significant time and effort. This study tackles this issue in a
resource-constrained environment, devoid of extensive datasets or pre-existing
models. We introduce Inconsistency Masks (IM), a novel approach that filters
uncertainty in image-pseudo-label pairs to substantially enhance segmentation
quality, surpassing traditional semi-supervised learning techniques. Employing
IM, we achieve strong segmentation results with as little as 10% labeled data,
across four diverse datasets and it further benefits from integration with
other techniques, indicating broad applicability. Notably on the ISIC 2018
dataset, three of our hybrid approaches even outperform models trained on the
fully labeled dataset. We also present a detailed comparative analysis of
prevalent semi-supervised learning strategies, all under uniform starting
conditions, to underline our approach's effectiveness and robustness. The full
code is available at: https://github.com/MichaelVorndran/InconsistencyMasks
