  Proper confidence calibration of deep neural networks is essential for
reliable predictions in safety-critical tasks. Miscalibration can lead to model
over-confidence and/or under-confidence; i.e., the model's confidence in its
prediction can be greater or less than the model's accuracy. Recent studies
have highlighted the over-confidence issue by introducing calibration
techniques and demonstrated success on various tasks. However, miscalibration
through under-confidence has not yet to receive much attention. In this paper,
we address the necessity of paying attention to the under-confidence issue. We
first introduce a novel metric, a miscalibration score, to identify the overall
and class-wise calibration status, including being over or under-confident. Our
proposed metric reveals the pitfalls of existing calibration techniques, where
they often overly calibrate the model and worsen under-confident predictions.
Then we utilize the class-wise miscalibration score as a proxy to design a
calibration technique that can tackle both over and under-confidence. We report
extensive experiments that show our proposed methods substantially
outperforming existing calibration techniques. We also validate our proposed
calibration technique on an automatic failure detection task with a
risk-coverage curve, reporting that our methods improve failure detection as
well as trustworthiness of the model. The code are available at
\url{https://github.com/AoShuang92/miscalibration_TS}.
