Remaining useful life (RUL) prediction has been receiving increasing attention in industry and academia due to its advantages in ensuring safety, reducing costs, and improving efficiency. For industrial manufacturing, the ball screw is an indispensable high-precision servo component. There is an urgent need to predict its RUL to guarantee machining precision. However, establishing a reliable and accurate physical model is a thorny issue as sophisticated mechanical characteristics are difficult to obtain. Besides, the limited degradation data reduces the prediction accuracy of data-driven methods. This article presents a hybrid RUL prediction architecture of ball screws. First, to track the precision degradation process, the backlash is proposed as a novel nonlinear precision indicator. Then, physical-and data-driven methods are strategically leveraged to reveal the precision degradation process of the ball screw, where the stochastic degradation model is constructed and its initial parameters are estimated by data-driven methods. The effectiveness of the method is experimentally demonstrated through the designed platform and compared with commonly used methods.