Table tennis robots have significantly advanced in performance owing to the rapid progress in deep learning and reinforcement learning technologies. However, these advancements often require a large number of training samples. Besides, research focused on the robot serve task remains relatively limited. In response to these problems, this paper proposes a sample-efficient adjustment-learning (SEAL) method for the serve task inspired by human experience in table tennis, which can inherently augment the available training samples without the need for additional sample collection. The adjustment learning does not require complex network structures but demonstrates superior performances. The models trained by adjustment learning have good generalization and robustness, that can adapt to different serve styles and reduce system transfer errors very efficiently. In addition, the random interpolation method during dataset generation stage is introduced, and the effectiveness of simultaneous learning in both joint space and Cartesian space is also demonstrated. For specific serve task, an accuracy of less than$\mathbf{3 0 ~ m m}$to any designated position at the first shot is achieved.