Pre-training followed by full fine-tuning has gradually been substituted by Parameter-Efficient Tuning (PET) in the field of computer vision tasks. PET has gained popularity, especially in the context of large-scale models, due to its ability to reduce transfer learning costs and conserve hardware resources. However, existing PET approaches primarily focus on recognition tasks and typically support uni-modal optimization, neglecting dense prediction tasks and vision language interactions. To address this limitation, we propose a novel PET framework called Bi-directional Intertwined Vision Language Efficient Tuning for Referring Image Segmentation (BarLeRIa), which leverages bi-directional intertwined vision language adapters to fully exploit the frozen pre-trained models' potential in cross-modal dense prediction tasks. In BarLeRIa, two different tuning modules are employed for efficient global and local attention, as well as an intertwined vision language tuning algorithm for efficient modal fusion. Extensive experiments conducted on challenging RefCOCO-related benchmarks demonstrating the superiority of BarLeRIa over prior PET methods with a significant margin, \emph{i.e.}, achieving an average improvement of 5.6\%. Remarkably, without requiring additional training datasets, BarLeRIa even surpasses SOTA full fine-tuning approaches.