  It is not an exaggeration to say that the recent progress in artificial
intelligence technology depends on large-scale and high-quality data.
Simultaneously, a prevalent issue exists everywhere: the budget for data
labeling is constrained. Active learning is a prominent approach for addressing
this issue, where valuable data for labeling is selected through a model and
utilized to iteratively adjust the model. However, due to the limited amount of
data in each iteration, the model is vulnerable to bias; thus, it is more
likely to yield overconfident predictions. In this paper, we present two novel
methods to address the problem of overconfidence that arises in the active
learning scenario. The first is an augmentation strategy named
Cross-Mix-and-Mix (CMaM), which aims to calibrate the model by expanding the
limited training distribution. The second is a selection strategy named Ranked
Margin Sampling (RankedMS), which prevents choosing data that leads to overly
confident predictions. Through various experiments and analyses, we are able to
demonstrate that our proposals facilitate efficient data selection by
alleviating overconfidence, even though they are readily applicable.
