Although traditional unsupervised domain adaptation (UDA) methods have proven effective in reducing domain gaps, their reliance on source domain data during adaptation often proves unfeasible in real-world applications. For instance, data access in a hospital setting is typically constrained due to patient privacy regulations. To address both the need for privacy protection and the mitigation of domain shifts between source and target domain data, we propose a novel two-step adversarial Source-Free Unsupervised Domain Adaptation (SFUDA) framework in this study. Our approach involves dividing the target domain data into confident and unconfident samples based on prediction entropy, using the Gumbel softmax technique. Confident samples are then treated as source domain data. In order to emulate adversarial training from traditional UDA methods, we employ a min-max loss in the first step, followed by a consistency loss in the second step. Additionally, we introduce a weight to penalize the L2-SP regularizer, which prevents excessive loss of source domain knowledge during optimization. Through extensive experiments on two distinct domain transfer challenges, our proposed SFUDA framework consistently outperforms other SFUDA methods. Remarkably, our approach even achieves competitive results when compared to state-of-the-art UDA methods, which benefit from direct access to source domain data. This demonstrates the potential of our novel SFUDA framework in addressing the limitations of traditional UDA methods while preserving patient privacy in sensitive applications.