Despite the promising performance of automated pain assessment methods, current methods suffer from performance generalization due to the lack of relatively large, diverse, and annotated pain datasets. Further, the majority of current methods do not allow responsible interaction between the model and user, and do not take different internal and external factors into consideration during the model's design and development. This article aims to provide an efficient cooperative learning framework for the lack of annotated data while facilitating responsible user communication and taking individual differences into consideration during the development of pain assessment models. Our results using body and muscle movement data, collected from wearable devices, demonstrate that the proposed framework is effective in leveraging both the human and the machine to efficiently learn and predict pain.