Acoustic feedback remains a significant challenge in open‐fitting digital hearing aids, as it severely degrades signal quality and restricts the maximum achievable stable gain. Adaptive Feedback Cancellation (AFC) is widely adopted to mitigate this issue; however, its performance is often compromised by bias in the feedback path estimation, particularly due to the high correlation between the loudspeaker and incoming signals—an issue exacerbated when the input is spectrally colored, such as in speech or music. The Prediction Error Method (PEM) has been extensively utilized to alleviate this bias by decorrelating the signals. To enhance the performance of PEM‐based AFC, we propose a novel switched method that introduces a novel update rule guided by a softclipping‐ based stability detector (SCSD), enabling dynamic switching between two AFC algorithms: PEM‐APSA is activated during steady‐state convergence, while NLMS is chosen when rapid adaptation is required, such as during abrupt changes in the acoustic feedback path or initialization state. The former, with a low step‐size, achieves low steady‐state error but suffers from slow adaptation, whereas the latter, utilizing a larger step‐size, provides faster convergence and tracking rates, especially robustness towards impulsive noise. By intelligently combining these methods, the proposed sw2 harnesses the advantages of both algorithms. Simulation results, obtained under various noisy environments and sudden changes in the feedback path, demonstrate that sw2 significantly accelerates adaptation and maintains low steady‐state error while preserving high signal quality and low complexity.