<p>Model calibration plays a pivotal role in refining computer models to faithfully represent the underlying physical processes. However, due to inherent theoretical imperfections and stochastic influences, biases between the model and reality are often inevitable, especially in small sample scenarios. While it may seem tempting to blindly minimize discrepancies, such an approach risks pushing the calibrated parameters further away from the true physical values. In response to this challenge, this paper introduces a novel correlation-based likelihood-free Bayesian calibration method, offering a framework that ensures the most similar alignment between the computer model and the physical process. The proposed method, which incorporates distance correlation-based parameter calibration, effectively retains the intrinsic discrepancies between the two domains, while simultaneously decoupling parameter uncertainty from model uncertainty. Furthermore, we introduce an innovative sequential sample infilling technique tailored for small sample scenarios, facilitating precise parameter estimation by independently updating design variables and calibration parameters. This approach ensures high-accuracy calibration with minimal computational overhead, where the parameters uncertainty quantification can be provided. Through rigorous comparisons with both numerical examples and an engineering case study involving thin-walled structures, we demonstrate that the proposed method delivers highly accurate parameter calibration and significantly enhances the predictive accuracy of the model.</p>