  We tackle the problem of Selective Classification where the objective is to
achieve the best performance on a predetermined ratio (coverage) of the
dataset. Recent state-of-the-art selective methods come with architectural
changes either via introducing a separate selection head or an extra abstention
logit. In this paper, we challenge the aforementioned methods. The results
suggest that the superior performance of state-of-the-art methods is owed to
training a more generalizable classifier rather than their proposed selection
mechanisms. We argue that the best performing selection mechanism should
instead be rooted in the classifier itself. Our proposed selection strategy
uses the classification scores and achieves better results by a significant
margin, consistently, across all coverages and all datasets, without any added
compute cost. Furthermore, inspired by semi-supervised learning, we propose an
entropy-based regularizer that improves the performance of selective
classification methods. Our proposed selection mechanism with the proposed
entropy-based regularizer achieves new state-of-the-art results.
