  Deep neural networks are becoming increasingly powerful and large and always
require more labelled data to be trained. However, since annotating data is
time-consuming, it is now necessary to develop systems that show good
performance while learning on a limited amount of data. These data must be
correctly chosen to obtain models that are still efficient. For this, the
systems must be able to determine which data should be annotated to achieve the
best results.
  In this paper, we propose four estimators to estimate the confidence of
object detection predictions. The first two are based on Monte Carlo dropout,
the third one on descriptive statistics and the last one on the detector
posterior probabilities. In the active learning framework, the three first
estimators show a significant improvement in performance for the detection of
document physical pages and text lines compared to a random selection of
images. We also show that the proposed estimator based on descriptive
statistics can replace MC dropout, reducing the computational cost without
compromising the performances.
