<p>Competing risk scenarios arise in many real-world situations where an event of interest is subject to multiple competing causes. This paper investigates a dependent competing risk framework under progressively type-II censored data within the context of accelerated life testing (ALT). In ALT, experimental units are exposed to different stress levels, and failure times corresponding to several competing causes are observed. To model the dependence between these competing causes, the Clayton copula is employed, capturing the interrelation between the failure modes. The Weibull distribution is used to represent the marginal behaviors of these competing risks. A Bayesian approach is adopted to estimate the parameters of the model, where the scale parameter of the Weibull distribution is regressed according to the Arrhenius relationship, reflecting the effect of stress levels. The Metropolis algorithm is utilized for posterior-based inferences after setting appropriate prior distributions for the model parameters. A numerical illustration using real data demonstrates the effectiveness of the proposed methodology in the dependent competing risk scenario under accelerated life testing.</p>