Independent MONAILabel Reviewer option without model/app requirements

Is your feature request related to a problem? Please describe.
Currently, launching the MONAILabel Reviewer Workflow requires a full segmentation or radiology app to be initialized. This creates several points of friction:

• Resource Waste: Loading a heavy model or bundle is unnecessary when the reviewer is only validating or correcting existing masks.
• Hardware Constraints: Reviewers (e.g., radiologists) often work on machines without high-end GPUs, making it difficult to "run" a full AI app just to access the review interface.
• UX Friction: The current "hack" of using a dummy app is unintuitive for new users who simply want to perform manual QA or label refinement on pre-existing data.

Describe the solution you'd like
I would like a lightweight, "Inference-free" or "Review-only" option to launch the MONAILabel server. This server-level mode should:

• Bypass Model Requirements: Start the server without requiring a specific model.pt or MONAI Bundle.
• Dedicated Entry Point: Provide a streamlined path for the Reviewer workflow that doesn't trigger AI inference engine initialization.
• Direct Dataset Mapping: Allow users to simply point the server to a dataset and an existing label set for validation/correction tasks.

Describe alternatives you've considered
Dummy Apps: Using a standard segmentation app as a placeholder to force the server to start. This is misleading and requires unnecessary app packaging and dummy model checkpoints.

Additional context
This change would allow MONAILabel to function as a standalone, lightweight tool for the manual QA and label refinement phase of the data lifecycle, independent of the training hardware.

The issue names no files, tests, or concrete entry points. Start by tracing the MONAILabel server launch path and Reviewer workflow to identify where model and app requirements are enforced. Done means a dataset and existing label set can launch a review-only server without a model, bundle, or inference initialization, with coverage for that path.