Biosensor-based droplet microfluidic high-throughput screening is extensively utilized in engineering microbial cell factories for the efficient production of various natural products. Under ideal conditions, biosensors detect product concentrations in the environment and emit corresponding measurable signals. However, bacteria cell growth rates and gene expression are significantly regulated in response to fluctuating environments, leading to substantial heterogeneity in cell density and gene expression among different subpopulations. In droplet environments, where cell density measurement is impractical, this heterogeneity can cause inaccuracies and an increase in false positives during biosensor-based screening, resulting in a significant additional workload for rescreening and verification processes. In this study, we developed modified dual-color, whole-cell Escherichia coli biosensors that report normalized fluorescent outputs, taking into account cell heterogeneity against various environmental stimuli. These biosensors were integrated with a droplet-based microfluidic platform to facilitate dual-color screening of libraries, achieving a superior enrichment ratio and increased droplet uniformity compared to single-color screening in the proof-of-concept attempt. In practical applications, the dual-color biosensor-assisted screening demonstrated 24.2 % and 11.9 % higher positive rates for wild-type Saccharopolyspora erythraea NRRL 23338 and industrial S0-derived mutagenesis libraries, respectively, compared to the single-color method. Additionally, S0-derived erythromycin hyperproducers with up to 19.6 % production improvement were successfully identified. This dual-color biosensor-assisted method enhances screening accuracy and reduces false positives by mitigating the impact of whole-cell biosensor heterogeneity, providing a universal strategy for engineering genetically encoded whole-cell biosensors. This advancement significantly improves high-throughput screening performance for various natural products in biosensor-driven applications.