  We present a novel region based active learning method for semantic image
segmentation, called MetaBox+. For acquisition, we train a meta regression
model to estimate the segment-wise Intersection over Union (IoU) of each
predicted segment of unlabeled images. This can be understood as an estimation
of segment-wise prediction quality. Queried regions are supposed to minimize to
competing targets, i.e., low predicted IoU values / segmentation quality and
low estimated annotation costs. For estimating the latter we propose a simple
but practical method for annotation cost estimation. We compare our method to
entropy based methods, where we consider the entropy as uncertainty of the
prediction. The comparison and analysis of the results provide insights into
annotation costs as well as robustness and variance of the methods. Numerical
experiments conducted with two different networks on the Cityscapes dataset
clearly demonstrate a reduction of annotation effort compared to random
acquisition. Noteworthily, we achieve 95%of the mean Intersection over Union
(mIoU), using MetaBox+ compared to when training with the full dataset, with
only 10.47% / 32.01% annotation effort for the two networks, respectively.
