Wrong predictions can lead to various problems in di erent application domains, e.g., improper medical diagnosis [25] and tra c accidents [16]. Enhancing the DL application systems by reducing wrong predictions of DL models in producing outputs is desirable. Studies [9, 51, 52] have shown that DL models are vulnerable to operational input samples that can lead them to produce incorrect predictions in natural scenarios [52], and the prediction con dences of many such failing samples exceed those well-intended guarding con dence levels [54]. For example, strong sunshine may cause the camera of a self-driving car to capture an image full of white pixels, resulting in a prediction failure with high con dence. A major bottleneck in developing DL applications is detecting these overcon dent failures from their deployed DL application systems. To reduce unreliable predictions, many real-world machine-learning-based application systems are equipped with rejectors to discard uncertain decisions [17]. In DL application systems, many existing techniques [6, 17, 45] construct their rejectors for DL models to address the incorrect prediction problem. For example, many recent studies [2, 8, 42, 49] have been conducted to enhance the defense ability of DL models against out-of-distribution (OOD) samples from unknown classes or arti cial examples that are very likely to guide DL models to yield failures.