Handling interval-censored data in survival analysis presents significant challenges, as the exact time to the event is only known to fall within predefined intervals. Common imputation strategies, such as those that use the lower bound, upper bound, or midpoint of the interval, often fail to capture the inherent uncertainty in the data, leading to biased or imprecise estimates. Prior studies have demonstrated the limitations of these approaches, particularly in accurately estimating survival probabilities and hazard ratios. To tackle these issues, we propose the Scaled Linear Redistribution Method, a new imputation technique aimed at overcoming the limitations of existing methods. The method redistributes imputed values within the interval, keeping their variation and basic statistical behavior. While our approach has not yet been implemented, it represents a promising direction for future research. We plan to evaluate its performance through a comprehensive simulation study, comparing its performance to that of traditional imputation methods and the Turnbull estimator, a widely used nonparametric method for interval-censored data.