This paper proposes a channel estimation method for hybrid wideband multiple-input-multiple-output (MIMO) systems in high-frequency bands, including millimeter-wave (mmWave) and sub-terahertz (sub-THz), in the presence of beam squint effects and array errors arising from hardware impairments and environmental time fluctuations such as thermal effects and dynamic motion of the array. Although conventional channel estimation methods calibrate array errors through offline operation with large training pilots, the calibration errors remain due to time-varying array errors. Therefore, the proposed channel estimation method calibrates array errors online with small pilot overhead. In the proposed method, array response matrices are explicitly decomposed into a small number of physical parameters including path gains, angles and array errors, which are iteratively estimated by alternating optimization based on a maximum likelihood (ML) criterion. To enhance the convergence performance, we introduce a switching mechanism from an ongrid algorithm to an off-gird algorithm depending on the estimation accuracy of the array error during algorithmic iterations. Furthermore, we introduce an approximate mutual coupling model to reduce the number of parameters. The reduction of parameters not only lowers computational complexity but also mitigates overfitting to noisy observations. Numerical simulations demonstrate that the proposed method effectively works online even with small pilot overhead in the presence of array errors.