Model-Agnostic Meta-learning (MAML) is a widely adopted few-shot learning (FSL) method designed to mitigate the dependency on large, labeled datasets of deep learning-based methods in medical imaging analysis. However, MAML's reliance on a fixed number of gradient descent (GD) steps for task adaptation results in computational inefficiency and task-level overfitting. To address this issue, we introduce Tra-MAML, which optimizes the balance between model adaptation capacity and computational efficiency through a trapezoidal step scheduler (TRA). The TRA scheduler dynamically adjusts the number of GD steps in the inner optimization loop: initially increasing the steps uniformly to reduce variance, maintaining the maximum number of steps to enhance adaptation capacity, and finally decreasing the steps uniformly to mitigate overfitting. Our evaluation of Tra-MAML against selected FSL methods across four medical imaging datasets demonstrates its superior performance. Notably, Tra-MAML outperforms MAML by 13.36% on the BreaKHis40X dataset in the 3-way 10-shot scenario.