  We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein
Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D
Computed Tomography (CT) segmentation. The proposed WD-UNet learns in a
semi-supervised way and accelerates learning convergence to meet or exceed the
prediction metrics of supervised learning models. Our method can be embedded
with different Active Learning (AL) strategies and different network
structures. The model is evaluated on 3D lung airway CT scans for medical
segmentation and show that the use of uncertainty metric, which is parametrized
as an input of query strategy, leads to more accurate prediction results than
some state-of-the-art Deep Learning (DL) supervised models, e.g.,3DUNet and 3D
CEUNet. Compared to the above supervised DL methods, our WD-UNet not only saves
the cost of annotation for radiologists but also saves computational resources.
WD-UNet uses a limited amount of annotated data (35% of the total) to achieve
better predictive metrics with a more efficient deep learning model algorithm.
