Patient similarity learning aims to use patient information such as electronic medical records and genetic data as input to calculate the pairwise similarity between patients, and it is becoming increasingly important in healthcare applications. However, in many cases, patient similarity learning models also need to forget some patient data. From the perspective of privacy, patients desire a tool to erase the impacts of their sensitive data from the trained patient similarity models. From the perspective of utility, if a patient similarity model’s utility is damaged by some bad patient data, the patient similarity model needs to forget such patient data to regain utility. Although some researchers have studied the problem of machine unlearning, existing methods cannot be directly applied to patient similarity learning as they fail to consider the comparative relationships among patients. In addition, they also fail to identify the optimal conditions of the local objective functions. In this paper, we fill in this gap by studying the unlearning problem in patient similarity learning. To unlearn the knowledge of a specific patient, we propose a novel erasable patient similarity learning framework, which enjoys the provable data removal guarantee and achieves high unlearning efficiency while keeping high model utility in patient similarity learning. We also conduct extensive experiments on real-world patient disease datasets to verify the desired properties of the proposed erasable framework.