  We propose a novel Active Learning framework capable to train effectively a
convolutional neural network for semantic segmentation of medical imaging, with
a limited amount of training labeled data. Our contribution is a practical
Cost-Effective Active Learning approach using dropout at test time as Monte
Carlo sampling to model the pixel-wise uncertainty and to analyze the image
information to improve the training performance. The source code of this
project is available at
https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .
